基于改进布谷鸟算法的火焰图像阈值分割算法

提出了改进的布谷鸟算法.在改进算法中,首先利用混沌理论进行种群的初始化,提高了种群的多样性.然后自适应选择利用莱维随机游走或者改进的蛙跳算法局部搜索方式更新新的解,提高了算法的局部搜索能力和算法的收敛速度.与其他算法相比较,改进算法显示了其强大的优化精度和高速度性,提高了图像的分割效率.

Enhanced minimum attribute reduction based on quantum-inspired shuffled frog leaping algorithm

Attribute reduction in the rough set theory is an important feature selection method, but finding a minimum attribute reduction has been proven to be a non-deterministic polynomial （NP）-hard problem. Therefore, it is necessary to investigate some fast and effective approximate algorithms. A novel and enhanced quantum-inspired shuffled frog leaping based minimum attribute reduction algorithm （QSFLAR） is proposed. Evolutionary frogs are represented by multi-state quantum bits, and both quantum rotation gate and quantum mutation operators are used to exploit the mechanisms of frog population diversity and convergence to the global optimum. The decomposed attribute subsets are co-evolved by the elitist frogs with a quantum-inspired shuffled frog leaping algorithm. The experimental results validate the better feasibility and effectiveness of QSFLAR, comparing with some representa- tive algorithms. Therefore, QSFLAR can be considered as a more competitive algorithm on the efficiency and accuracy for minimum attribute reduction.

基于考场编排的改进分治混洗算法研究

考场编排是日常考试管理和考务工作的一项重要工作。科学、合理、高效的编排考场,对于严肃考风考纪、提升考试的公信度有着十分重要的意义。常用考场编排系统中所采用的分治算法或者混洗算法都不能很好地将同一班级的考生位置有效的分开,直接影响考试的公平性和合理性。通过对分治算法与混洗算法的仔细研究,结合日常考场编排的要求,提出一种将分治算法与混洗算法合而为一的改进考场编排算法,并在等级考试、职业资格考试考场编排中广泛使用,经验证该算法结果稳定可靠。